Modeling of Cognitive Mechanisms in Second Language Acquisition and Study on the Effectiveness of English Learning Interventions Driven by Big Data
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Abstract
Within the dual context of digital educational transformation and second language acquisition research, traditional English learning interventions fail to account for the precise cognitive mechanisms and individual variations among learners, resulting in a lack of data-driven scientific support to meet personalized learning needs. Big data technology, with its core advantages such as multi-dimensional data collection, deep correlation mining, and dynamic modeling analysis, provides a technical path for breaking the black box of second language acquisition cognitive mechanisms and building a precise learning intervention system. This article focuses on the modeling of cognitive mechanisms in second language acquisition and English learning interventions driven by big data, and constructs a five in one research framework of “theoretical integration data collection mechanism modeling intervention design empirical verification”. The use of EEG and eye-tracking data makes the framework relevant to bioelectrical signal-assisted learning analysis.
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